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README.md
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---
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| 2 |
+
pretty_name: AutoDataBench Function Calling Resources
|
| 3 |
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tags:
|
| 4 |
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- autodatabench
|
| 5 |
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- function-calling
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| 6 |
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- tool-use
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| 7 |
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---
|
| 8 |
+
|
| 9 |
+
# AutoDataBench Function Calling Resources
|
| 10 |
+
|
| 11 |
+
Public resources for the function-calling task in
|
| 12 |
+
[AutoDataBench](https://github.com/AutoDataBench/AutoDataBench). See the
|
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[paper](https://arxiv.org/abs/2609.40097) for the benchmark setting.
|
| 14 |
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| 15 |
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## Contents
|
| 16 |
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| 17 |
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```text
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| 18 |
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data/function_call_v1/pool_agent.jsonl
|
| 19 |
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models/Qwen2-1.5B-Instruct/
|
| 20 |
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models/Qwen3-4B-Instruct-2507/
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| 21 |
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models/Qwen3-Embedding-0.6B/
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```
|
| 23 |
+
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| 24 |
+
`pool_agent.jsonl` is the 40,001-row noisy single-turn function-calling pool
|
| 25 |
+
available to the data agent. It does not expose the noise labels used to build
|
| 26 |
+
the pool.
|
| 27 |
+
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| 28 |
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| Model | Role | Original model |
|
| 29 |
+
| --- | --- | --- |
|
| 30 |
+
| Qwen2-1.5B-Instruct | Fixed function-calling base model | [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct) |
|
| 31 |
+
| Qwen3-4B-Instruct-2507 | Agent-callable generation model | [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) |
|
| 32 |
+
| Qwen3-Embedding-0.6B | Agent-callable embedding model | [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) |
|
| 33 |
+
|
| 34 |
+
## Evaluation data
|
| 35 |
+
|
| 36 |
+
The held-out in-domain test set is intentionally excluded. The BFCL v3 OOD
|
| 37 |
+
guard is also not duplicated here; maintainers should obtain it from the
|
| 38 |
+
[Berkeley Function Calling Leaderboard](https://huggingface.co/datasets/gorilla-llm/Berkeley-Function-Calling-Leaderboard)
|
| 39 |
+
and keep gold answers outside the agent sandbox.
|
| 40 |
+
|
| 41 |
+
## Use with AutoDataBench
|
| 42 |
+
|
| 43 |
+
Copy or symlink `data/` and `models/` into the AutoDataBench repository. The
|
| 44 |
+
paths already match the default task configuration. Point the generation and
|
| 45 |
+
embedding servers at the local auxiliary-model directories if needed.
|
| 46 |
+
|
| 47 |
+
`MANIFEST.sha256` contains checksums for every distributed file.
|
| 48 |
+
|
| 49 |
+
Model and dataset components retain their upstream licenses. Consult the model
|
| 50 |
+
cards and source datasets before redistribution or commercial use.
|
data/function_call_v1/pool_agent.jsonl
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version https://git-lfs.github.com/spec/v1
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oid sha256:183d5ad6caf631e00ddfdb76f1a34f308e5652f73a21b713df69c321078f0c27
|
| 3 |
+
size 60382232
|
models/Qwen2-1.5B-Instruct/.gitattributes
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*.npy filter=lfs diff=lfs merge=lfs -text
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| 15 |
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*.npz filter=lfs diff=lfs merge=lfs -text
|
| 16 |
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*.onnx filter=lfs diff=lfs merge=lfs -text
|
| 17 |
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*.ot filter=lfs diff=lfs merge=lfs -text
|
| 18 |
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*.parquet filter=lfs diff=lfs merge=lfs -text
|
| 19 |
+
*.pb filter=lfs diff=lfs merge=lfs -text
|
| 20 |
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*.pickle filter=lfs diff=lfs merge=lfs -text
|
| 21 |
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*.pkl filter=lfs diff=lfs merge=lfs -text
|
| 22 |
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*.pt filter=lfs diff=lfs merge=lfs -text
|
| 23 |
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*.pth filter=lfs diff=lfs merge=lfs -text
|
| 24 |
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*.rar filter=lfs diff=lfs merge=lfs -text
|
| 25 |
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*.safetensors filter=lfs diff=lfs merge=lfs -text
|
| 26 |
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
| 27 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
| 28 |
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*.tar filter=lfs diff=lfs merge=lfs -text
|
| 29 |
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*.tflite filter=lfs diff=lfs merge=lfs -text
|
| 30 |
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*.tgz filter=lfs diff=lfs merge=lfs -text
|
| 31 |
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*.wasm filter=lfs diff=lfs merge=lfs -text
|
| 32 |
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*.xz filter=lfs diff=lfs merge=lfs -text
|
| 33 |
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*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
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*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
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models/Qwen2-1.5B-Instruct/LICENSE
ADDED
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models/Qwen2-1.5B-Instruct/README.md
ADDED
|
@@ -0,0 +1,93 @@
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|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
language:
|
| 4 |
+
- en
|
| 5 |
+
pipeline_tag: text-generation
|
| 6 |
+
tags:
|
| 7 |
+
- chat
|
| 8 |
+
---
|
| 9 |
+
|
| 10 |
+
# Qwen2-1.5B-Instruct
|
| 11 |
+
|
| 12 |
+
## Introduction
|
| 13 |
+
|
| 14 |
+
Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 1.5B Qwen2 model.
|
| 15 |
+
|
| 16 |
+
Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc.
|
| 17 |
+
|
| 18 |
+
For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/), [GitHub](https://github.com/QwenLM/Qwen2), and [Documentation](https://qwen.readthedocs.io/en/latest/).
|
| 19 |
+
<br>
|
| 20 |
+
|
| 21 |
+
## Model Details
|
| 22 |
+
Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes.
|
| 23 |
+
|
| 24 |
+
## Training details
|
| 25 |
+
We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization.
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
## Requirements
|
| 29 |
+
The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
|
| 30 |
+
```
|
| 31 |
+
KeyError: 'qwen2'
|
| 32 |
+
```
|
| 33 |
+
|
| 34 |
+
## Quickstart
|
| 35 |
+
|
| 36 |
+
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
|
| 37 |
+
|
| 38 |
+
```python
|
| 39 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
| 40 |
+
device = "cuda" # the device to load the model onto
|
| 41 |
+
|
| 42 |
+
model = AutoModelForCausalLM.from_pretrained(
|
| 43 |
+
"Qwen/Qwen2-1.5B-Instruct",
|
| 44 |
+
torch_dtype="auto",
|
| 45 |
+
device_map="auto"
|
| 46 |
+
)
|
| 47 |
+
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-1.5B-Instruct")
|
| 48 |
+
|
| 49 |
+
prompt = "Give me a short introduction to large language model."
|
| 50 |
+
messages = [
|
| 51 |
+
{"role": "system", "content": "You are a helpful assistant."},
|
| 52 |
+
{"role": "user", "content": prompt}
|
| 53 |
+
]
|
| 54 |
+
text = tokenizer.apply_chat_template(
|
| 55 |
+
messages,
|
| 56 |
+
tokenize=False,
|
| 57 |
+
add_generation_prompt=True
|
| 58 |
+
)
|
| 59 |
+
model_inputs = tokenizer([text], return_tensors="pt").to(device)
|
| 60 |
+
|
| 61 |
+
generated_ids = model.generate(
|
| 62 |
+
model_inputs.input_ids,
|
| 63 |
+
max_new_tokens=512
|
| 64 |
+
)
|
| 65 |
+
generated_ids = [
|
| 66 |
+
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
|
| 67 |
+
]
|
| 68 |
+
|
| 69 |
+
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
| 70 |
+
```
|
| 71 |
+
|
| 72 |
+
## Evaluation
|
| 73 |
+
|
| 74 |
+
We briefly compare Qwen2-1.5B-Instruct with Qwen1.5-1.8B-Chat. The results are as follows:
|
| 75 |
+
|
| 76 |
+
| Datasets | Qwen1.5-0.5B-Chat | **Qwen2-0.5B-Instruct** | Qwen1.5-1.8B-Chat | **Qwen2-1.5B-Instruct** |
|
| 77 |
+
| :--- | :---: | :---: | :---: | :---: |
|
| 78 |
+
| MMLU | 35.0 | **37.9** | 43.7 | **52.4** |
|
| 79 |
+
| HumanEval | 9.1 | **17.1** | 25.0 | **37.8** |
|
| 80 |
+
| GSM8K | 11.3 | **40.1** | 35.3 | **61.6** |
|
| 81 |
+
| C-Eval | 37.2 | **45.2** | 55.3 | **63.8** |
|
| 82 |
+
| IFEval (Prompt Strict-Acc.) | 14.6 | **20.0** | 16.8 | **29.0** |
|
| 83 |
+
|
| 84 |
+
## Citation
|
| 85 |
+
|
| 86 |
+
If you find our work helpful, feel free to give us a cite.
|
| 87 |
+
|
| 88 |
+
```
|
| 89 |
+
@article{qwen2,
|
| 90 |
+
title={Qwen2 Technical Report},
|
| 91 |
+
year={2024}
|
| 92 |
+
}
|
| 93 |
+
```
|
models/Qwen2-1.5B-Instruct/config.json
ADDED
|
@@ -0,0 +1,27 @@
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|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"Qwen2ForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"attention_dropout": 0.0,
|
| 6 |
+
"bos_token_id": 151643,
|
| 7 |
+
"eos_token_id": 151645,
|
| 8 |
+
"hidden_act": "silu",
|
| 9 |
+
"hidden_size": 1536,
|
| 10 |
+
"initializer_range": 0.02,
|
| 11 |
+
"intermediate_size": 8960,
|
| 12 |
+
"max_position_embeddings": 32768,
|
| 13 |
+
"max_window_layers": 28,
|
| 14 |
+
"model_type": "qwen2",
|
| 15 |
+
"num_attention_heads": 12,
|
| 16 |
+
"num_hidden_layers": 28,
|
| 17 |
+
"num_key_value_heads": 2,
|
| 18 |
+
"rms_norm_eps": 1e-06,
|
| 19 |
+
"rope_theta": 1000000.0,
|
| 20 |
+
"sliding_window": 32768,
|
| 21 |
+
"tie_word_embeddings": true,
|
| 22 |
+
"torch_dtype": "bfloat16",
|
| 23 |
+
"transformers_version": "4.40.1",
|
| 24 |
+
"use_cache": true,
|
| 25 |
+
"use_sliding_window": false,
|
| 26 |
+
"vocab_size": 151936
|
| 27 |
+
}
|
models/Qwen2-1.5B-Instruct/generation_config.json
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token_id": 151643,
|
| 3 |
+
"pad_token_id": 151643,
|
| 4 |
+
"do_sample": true,
|
| 5 |
+
"eos_token_id": [
|
| 6 |
+
151645,
|
| 7 |
+
151643
|
| 8 |
+
],
|
| 9 |
+
"repetition_penalty": 1.1,
|
| 10 |
+
"temperature": 0.7,
|
| 11 |
+
"top_p": 0.8,
|
| 12 |
+
"top_k": 20,
|
| 13 |
+
"transformers_version": "4.37.0"
|
| 14 |
+
}
|
models/Qwen2-1.5B-Instruct/merges.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
models/Qwen2-1.5B-Instruct/model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:302e327795994403cb1e3cb6a3345c76b246b894d14078c936b570c83a4e9057
|
| 3 |
+
size 3087467144
|
models/Qwen2-1.5B-Instruct/tokenizer.json
ADDED
|
The diff for this file is too large to render.
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|
|
|
models/Qwen2-1.5B-Instruct/tokenizer_config.json
ADDED
|
@@ -0,0 +1,40 @@
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|
|
|
|
|
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|
| 1 |
+
{
|
| 2 |
+
"add_prefix_space": false,
|
| 3 |
+
"added_tokens_decoder": {
|
| 4 |
+
"151643": {
|
| 5 |
+
"content": "<|endoftext|>",
|
| 6 |
+
"lstrip": false,
|
| 7 |
+
"normalized": false,
|
| 8 |
+
"rstrip": false,
|
| 9 |
+
"single_word": false,
|
| 10 |
+
"special": true
|
| 11 |
+
},
|
| 12 |
+
"151644": {
|
| 13 |
+
"content": "<|im_start|>",
|
| 14 |
+
"lstrip": false,
|
| 15 |
+
"normalized": false,
|
| 16 |
+
"rstrip": false,
|
| 17 |
+
"single_word": false,
|
| 18 |
+
"special": true
|
| 19 |
+
},
|
| 20 |
+
"151645": {
|
| 21 |
+
"content": "<|im_end|>",
|
| 22 |
+
"lstrip": false,
|
| 23 |
+
"normalized": false,
|
| 24 |
+
"rstrip": false,
|
| 25 |
+
"single_word": false,
|
| 26 |
+
"special": true
|
| 27 |
+
}
|
| 28 |
+
},
|
| 29 |
+
"additional_special_tokens": ["<|im_start|>", "<|im_end|>"],
|
| 30 |
+
"bos_token": null,
|
| 31 |
+
"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
| 32 |
+
"clean_up_tokenization_spaces": false,
|
| 33 |
+
"eos_token": "<|im_end|>",
|
| 34 |
+
"errors": "replace",
|
| 35 |
+
"model_max_length": 32768,
|
| 36 |
+
"pad_token": "<|endoftext|>",
|
| 37 |
+
"split_special_tokens": false,
|
| 38 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 39 |
+
"unk_token": null
|
| 40 |
+
}
|
models/Qwen2-1.5B-Instruct/vocab.json
ADDED
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|
|
models/Qwen3-4B-Instruct-2507/.gitattributes
ADDED
|
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| 1 |
+
*.7z filter=lfs diff=lfs merge=lfs -text
|
| 2 |
+
*.arrow filter=lfs diff=lfs merge=lfs -text
|
| 3 |
+
*.bin filter=lfs diff=lfs merge=lfs -text
|
| 4 |
+
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
| 5 |
+
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
| 6 |
+
*.ftz filter=lfs diff=lfs merge=lfs -text
|
| 7 |
+
*.gz filter=lfs diff=lfs merge=lfs -text
|
| 8 |
+
*.h5 filter=lfs diff=lfs merge=lfs -text
|
| 9 |
+
*.joblib filter=lfs diff=lfs merge=lfs -text
|
| 10 |
+
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
| 11 |
+
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
| 12 |
+
*.model filter=lfs diff=lfs merge=lfs -text
|
| 13 |
+
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
| 14 |
+
*.npy filter=lfs diff=lfs merge=lfs -text
|
| 15 |
+
*.npz filter=lfs diff=lfs merge=lfs -text
|
| 16 |
+
*.onnx filter=lfs diff=lfs merge=lfs -text
|
| 17 |
+
*.ot filter=lfs diff=lfs merge=lfs -text
|
| 18 |
+
*.parquet filter=lfs diff=lfs merge=lfs -text
|
| 19 |
+
*.pb filter=lfs diff=lfs merge=lfs -text
|
| 20 |
+
*.pickle filter=lfs diff=lfs merge=lfs -text
|
| 21 |
+
*.pkl filter=lfs diff=lfs merge=lfs -text
|
| 22 |
+
*.pt filter=lfs diff=lfs merge=lfs -text
|
| 23 |
+
*.pth filter=lfs diff=lfs merge=lfs -text
|
| 24 |
+
*.rar filter=lfs diff=lfs merge=lfs -text
|
| 25 |
+
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
| 26 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
| 27 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
| 28 |
+
*.tar filter=lfs diff=lfs merge=lfs -text
|
| 29 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
| 30 |
+
*.tgz filter=lfs diff=lfs merge=lfs -text
|
| 31 |
+
*.wasm filter=lfs diff=lfs merge=lfs -text
|
| 32 |
+
*.xz filter=lfs diff=lfs merge=lfs -text
|
| 33 |
+
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
+
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 36 |
+
tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
models/Qwen3-4B-Instruct-2507/LICENSE
ADDED
|
@@ -0,0 +1,202 @@
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models/Qwen3-4B-Instruct-2507/README.md
ADDED
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|
| 1 |
+
---
|
| 2 |
+
library_name: transformers
|
| 3 |
+
license: apache-2.0
|
| 4 |
+
license_link: https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507/blob/main/LICENSE
|
| 5 |
+
pipeline_tag: text-generation
|
| 6 |
+
---
|
| 7 |
+
|
| 8 |
+
# Qwen3-4B-Instruct-2507
|
| 9 |
+
<a href="https://chat.qwen.ai" target="_blank" style="margin: 2px;">
|
| 10 |
+
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
|
| 11 |
+
</a>
|
| 12 |
+
|
| 13 |
+
## Highlights
|
| 14 |
+
|
| 15 |
+
We introduce the updated version of the **Qwen3-4B non-thinking mode**, named **Qwen3-4B-Instruct-2507**, featuring the following key enhancements:
|
| 16 |
+
|
| 17 |
+
- **Significant improvements** in general capabilities, including **instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage**.
|
| 18 |
+
- **Substantial gains** in long-tail knowledge coverage across **multiple languages**.
|
| 19 |
+
- **Markedly better alignment** with user preferences in **subjective and open-ended tasks**, enabling more helpful responses and higher-quality text generation.
|
| 20 |
+
- **Enhanced capabilities** in **256K long-context understanding**.
|
| 21 |
+
|
| 22 |
+

|
| 23 |
+
|
| 24 |
+
## Model Overview
|
| 25 |
+
|
| 26 |
+
**Qwen3-4B-Instruct-2507** has the following features:
|
| 27 |
+
- Type: Causal Language Models
|
| 28 |
+
- Training Stage: Pretraining & Post-training
|
| 29 |
+
- Number of Parameters: 4.0B
|
| 30 |
+
- Number of Paramaters (Non-Embedding): 3.6B
|
| 31 |
+
- Number of Layers: 36
|
| 32 |
+
- Number of Attention Heads (GQA): 32 for Q and 8 for KV
|
| 33 |
+
- Context Length: **262,144 natively**.
|
| 34 |
+
|
| 35 |
+
**NOTE: This model supports only non-thinking mode and does not generate ``<think></think>`` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.**
|
| 36 |
+
|
| 37 |
+
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
## Performance
|
| 41 |
+
|
| 42 |
+
| | GPT-4.1-nano-2025-04-14 | Qwen3-30B-A3B Non-Thinking | Qwen3-4B Non-Thinking | Qwen3-4B-Instruct-2507 |
|
| 43 |
+
|--- | --- | --- | --- | --- |
|
| 44 |
+
| **Knowledge** | | | |
|
| 45 |
+
| MMLU-Pro | 62.8 | 69.1 | 58.0 | **69.6** |
|
| 46 |
+
| MMLU-Redux | 80.2 | 84.1 | 77.3 | **84.2** |
|
| 47 |
+
| GPQA | 50.3 | 54.8 | 41.7 | **62.0** |
|
| 48 |
+
| SuperGPQA | 32.2 | 42.2 | 32.0 | **42.8** |
|
| 49 |
+
| **Reasoning** | | | |
|
| 50 |
+
| AIME25 | 22.7 | 21.6 | 19.1 | **47.4** |
|
| 51 |
+
| HMMT25 | 9.7 | 12.0 | 12.1 | **31.0** |
|
| 52 |
+
| ZebraLogic | 14.8 | 33.2 | 35.2 | **80.2** |
|
| 53 |
+
| LiveBench 20241125 | 41.5 | 59.4 | 48.4 | **63.0** |
|
| 54 |
+
| **Coding** | | | |
|
| 55 |
+
| LiveCodeBench v6 (25.02-25.05) | 31.5 | 29.0 | 26.4 | **35.1** |
|
| 56 |
+
| MultiPL-E | 76.3 | 74.6 | 66.6 | **76.8** |
|
| 57 |
+
| Aider-Polyglot | 9.8 | **24.4** | 13.8 | 12.9 |
|
| 58 |
+
| **Alignment** | | | |
|
| 59 |
+
| IFEval | 74.5 | **83.7** | 81.2 | 83.4 |
|
| 60 |
+
| Arena-Hard v2* | 15.9 | 24.8 | 9.5 | **43.4** |
|
| 61 |
+
| Creative Writing v3 | 72.7 | 68.1 | 53.6 | **83.5** |
|
| 62 |
+
| WritingBench | 66.9 | 72.2 | 68.5 | **83.4** |
|
| 63 |
+
| **Agent** | | | |
|
| 64 |
+
| BFCL-v3 | 53.0 | 58.6 | 57.6 | **61.9** |
|
| 65 |
+
| TAU1-Retail | 23.5 | 38.3 | 24.3 | **48.7** |
|
| 66 |
+
| TAU1-Airline | 14.0 | 18.0 | 16.0 | **32.0** |
|
| 67 |
+
| TAU2-Retail | - | 31.6 | 28.1 | **40.4** |
|
| 68 |
+
| TAU2-Airline | - | 18.0 | 12.0 | **24.0** |
|
| 69 |
+
| TAU2-Telecom | - | **18.4** | 17.5 | 13.2 |
|
| 70 |
+
| **Multilingualism** | | | |
|
| 71 |
+
| MultiIF | 60.7 | **70.8** | 61.3 | 69.0 |
|
| 72 |
+
| MMLU-ProX | 56.2 | **65.1** | 49.6 | 61.6 |
|
| 73 |
+
| INCLUDE | 58.6 | **67.8** | 53.8 | 60.1 |
|
| 74 |
+
| PolyMATH | 15.6 | 23.3 | 16.6 | **31.1** |
|
| 75 |
+
|
| 76 |
+
*: For reproducibility, we report the win rates evaluated by GPT-4.1.
|
| 77 |
+
|
| 78 |
+
|
| 79 |
+
## Quickstart
|
| 80 |
+
|
| 81 |
+
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
|
| 82 |
+
|
| 83 |
+
With `transformers<4.51.0`, you will encounter the following error:
|
| 84 |
+
```
|
| 85 |
+
KeyError: 'qwen3'
|
| 86 |
+
```
|
| 87 |
+
|
| 88 |
+
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
|
| 89 |
+
```python
|
| 90 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
| 91 |
+
|
| 92 |
+
model_name = "Qwen/Qwen3-4B-Instruct-2507"
|
| 93 |
+
|
| 94 |
+
# load the tokenizer and the model
|
| 95 |
+
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
| 96 |
+
model = AutoModelForCausalLM.from_pretrained(
|
| 97 |
+
model_name,
|
| 98 |
+
torch_dtype="auto",
|
| 99 |
+
device_map="auto"
|
| 100 |
+
)
|
| 101 |
+
|
| 102 |
+
# prepare the model input
|
| 103 |
+
prompt = "Give me a short introduction to large language model."
|
| 104 |
+
messages = [
|
| 105 |
+
{"role": "user", "content": prompt}
|
| 106 |
+
]
|
| 107 |
+
text = tokenizer.apply_chat_template(
|
| 108 |
+
messages,
|
| 109 |
+
tokenize=False,
|
| 110 |
+
add_generation_prompt=True,
|
| 111 |
+
)
|
| 112 |
+
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
|
| 113 |
+
|
| 114 |
+
# conduct text completion
|
| 115 |
+
generated_ids = model.generate(
|
| 116 |
+
**model_inputs,
|
| 117 |
+
max_new_tokens=16384
|
| 118 |
+
)
|
| 119 |
+
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
|
| 120 |
+
|
| 121 |
+
content = tokenizer.decode(output_ids, skip_special_tokens=True)
|
| 122 |
+
|
| 123 |
+
print("content:", content)
|
| 124 |
+
```
|
| 125 |
+
|
| 126 |
+
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
|
| 127 |
+
- SGLang:
|
| 128 |
+
```shell
|
| 129 |
+
python -m sglang.launch_server --model-path Qwen/Qwen3-4B-Instruct-2507 --context-length 262144
|
| 130 |
+
```
|
| 131 |
+
- vLLM:
|
| 132 |
+
```shell
|
| 133 |
+
vllm serve Qwen/Qwen3-4B-Instruct-2507 --max-model-len 262144
|
| 134 |
+
```
|
| 135 |
+
|
| 136 |
+
**Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768`.**
|
| 137 |
+
|
| 138 |
+
For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
|
| 139 |
+
|
| 140 |
+
## Agentic Use
|
| 141 |
+
|
| 142 |
+
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
|
| 143 |
+
|
| 144 |
+
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
|
| 145 |
+
```python
|
| 146 |
+
from qwen_agent.agents import Assistant
|
| 147 |
+
|
| 148 |
+
# Define LLM
|
| 149 |
+
llm_cfg = {
|
| 150 |
+
'model': 'Qwen3-4B-Instruct-2507',
|
| 151 |
+
|
| 152 |
+
# Use a custom endpoint compatible with OpenAI API:
|
| 153 |
+
'model_server': 'http://localhost:8000/v1', # api_base
|
| 154 |
+
'api_key': 'EMPTY',
|
| 155 |
+
}
|
| 156 |
+
|
| 157 |
+
# Define Tools
|
| 158 |
+
tools = [
|
| 159 |
+
{'mcpServers': { # You can specify the MCP configuration file
|
| 160 |
+
'time': {
|
| 161 |
+
'command': 'uvx',
|
| 162 |
+
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
|
| 163 |
+
},
|
| 164 |
+
"fetch": {
|
| 165 |
+
"command": "uvx",
|
| 166 |
+
"args": ["mcp-server-fetch"]
|
| 167 |
+
}
|
| 168 |
+
}
|
| 169 |
+
},
|
| 170 |
+
'code_interpreter', # Built-in tools
|
| 171 |
+
]
|
| 172 |
+
|
| 173 |
+
# Define Agent
|
| 174 |
+
bot = Assistant(llm=llm_cfg, function_list=tools)
|
| 175 |
+
|
| 176 |
+
# Streaming generation
|
| 177 |
+
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
|
| 178 |
+
for responses in bot.run(messages=messages):
|
| 179 |
+
pass
|
| 180 |
+
print(responses)
|
| 181 |
+
```
|
| 182 |
+
|
| 183 |
+
## Best Practices
|
| 184 |
+
|
| 185 |
+
To achieve optimal performance, we recommend the following settings:
|
| 186 |
+
|
| 187 |
+
1. **Sampling Parameters**:
|
| 188 |
+
- We suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
|
| 189 |
+
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
|
| 190 |
+
|
| 191 |
+
2. **Adequate Output Length**: We recommend using an output length of 16,384 tokens for most queries, which is adequate for instruct models.
|
| 192 |
+
|
| 193 |
+
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
|
| 194 |
+
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
|
| 195 |
+
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
|
| 196 |
+
|
| 197 |
+
### Citation
|
| 198 |
+
|
| 199 |
+
If you find our work helpful, feel free to give us a cite.
|
| 200 |
+
|
| 201 |
+
```
|
| 202 |
+
@misc{qwen3technicalreport,
|
| 203 |
+
title={Qwen3 Technical Report},
|
| 204 |
+
author={Qwen Team},
|
| 205 |
+
year={2025},
|
| 206 |
+
eprint={2505.09388},
|
| 207 |
+
archivePrefix={arXiv},
|
| 208 |
+
primaryClass={cs.CL},
|
| 209 |
+
url={https://arxiv.org/abs/2505.09388},
|
| 210 |
+
}
|
| 211 |
+
```
|
models/Qwen3-4B-Instruct-2507/config.json
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
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|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"Qwen3ForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"attention_bias": false,
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"bos_token_id": 151643,
|
| 8 |
+
"eos_token_id": 151645,
|
| 9 |
+
"head_dim": 128,
|
| 10 |
+
"hidden_act": "silu",
|
| 11 |
+
"hidden_size": 2560,
|
| 12 |
+
"initializer_range": 0.02,
|
| 13 |
+
"intermediate_size": 9728,
|
| 14 |
+
"max_position_embeddings": 262144,
|
| 15 |
+
"max_window_layers": 36,
|
| 16 |
+
"model_type": "qwen3",
|
| 17 |
+
"num_attention_heads": 32,
|
| 18 |
+
"num_hidden_layers": 36,
|
| 19 |
+
"num_key_value_heads": 8,
|
| 20 |
+
"rms_norm_eps": 1e-06,
|
| 21 |
+
"rope_scaling": null,
|
| 22 |
+
"rope_theta": 5000000,
|
| 23 |
+
"sliding_window": null,
|
| 24 |
+
"tie_word_embeddings": true,
|
| 25 |
+
"torch_dtype": "bfloat16",
|
| 26 |
+
"transformers_version": "4.51.0",
|
| 27 |
+
"use_cache": true,
|
| 28 |
+
"use_sliding_window": false,
|
| 29 |
+
"vocab_size": 151936
|
| 30 |
+
}
|
models/Qwen3-4B-Instruct-2507/generation_config.json
ADDED
|
@@ -0,0 +1,13 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token_id": 151643,
|
| 3 |
+
"do_sample": true,
|
| 4 |
+
"eos_token_id": [
|
| 5 |
+
151645,
|
| 6 |
+
151643
|
| 7 |
+
],
|
| 8 |
+
"pad_token_id": 151643,
|
| 9 |
+
"temperature": 0.7,
|
| 10 |
+
"top_k": 20,
|
| 11 |
+
"top_p": 0.8,
|
| 12 |
+
"transformers_version": "4.51.0"
|
| 13 |
+
}
|
models/Qwen3-4B-Instruct-2507/merges.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
models/Qwen3-4B-Instruct-2507/model-00001-of-00003.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:75311d91bb08cf0b882913da464a1e722a31fb44db35208663487efb7a3d8ed6
|
| 3 |
+
size 3957900840
|
models/Qwen3-4B-Instruct-2507/model-00002-of-00003.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:0b48adbb1f60e901153d91907ba11ce63bd4b8b584482e730f48808d055dfba1
|
| 3 |
+
size 3987450520
|
models/Qwen3-4B-Instruct-2507/model-00003-of-00003.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:7dd39ccca5e4de123c74c14af44c9bf2eb75df33b4614382af0134528e060d5d
|
| 3 |
+
size 99630640
|
models/Qwen3-4B-Instruct-2507/model.safetensors.index.json
ADDED
|
@@ -0,0 +1,405 @@
|
|
|
|
|
|
|
|
|
|
|
|
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| 136 |
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"rstrip": false,
|
| 137 |
+
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|
| 138 |
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"special": false
|
| 139 |
+
},
|
| 140 |
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"151660": {
|
| 141 |
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"content": "<|fim_middle|>",
|
| 142 |
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"lstrip": false,
|
| 143 |
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"normalized": false,
|
| 144 |
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"rstrip": false,
|
| 145 |
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"single_word": false,
|
| 146 |
+
"special": false
|
| 147 |
+
},
|
| 148 |
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"151661": {
|
| 149 |
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"content": "<|fim_suffix|>",
|
| 150 |
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"lstrip": false,
|
| 151 |
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"normalized": false,
|
| 152 |
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|
| 153 |
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|
| 154 |
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|
| 155 |
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|
| 156 |
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"151662": {
|
| 157 |
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|
| 158 |
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|
| 159 |
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|
| 160 |
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|
| 161 |
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|
| 162 |
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"special": false
|
| 163 |
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},
|
| 164 |
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"151663": {
|
| 165 |
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"content": "<|repo_name|>",
|
| 166 |
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"lstrip": false,
|
| 167 |
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"normalized": false,
|
| 168 |
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"rstrip": false,
|
| 169 |
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"single_word": false,
|
| 170 |
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"special": false
|
| 171 |
+
},
|
| 172 |
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"151664": {
|
| 173 |
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"content": "<|file_sep|>",
|
| 174 |
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|
| 175 |
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"normalized": false,
|
| 176 |
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"rstrip": false,
|
| 177 |
+
"single_word": false,
|
| 178 |
+
"special": false
|
| 179 |
+
},
|
| 180 |
+
"151665": {
|
| 181 |
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"content": "<tool_response>",
|
| 182 |
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"lstrip": false,
|
| 183 |
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"normalized": false,
|
| 184 |
+
"rstrip": false,
|
| 185 |
+
"single_word": false,
|
| 186 |
+
"special": false
|
| 187 |
+
},
|
| 188 |
+
"151666": {
|
| 189 |
+
"content": "</tool_response>",
|
| 190 |
+
"lstrip": false,
|
| 191 |
+
"normalized": false,
|
| 192 |
+
"rstrip": false,
|
| 193 |
+
"single_word": false,
|
| 194 |
+
"special": false
|
| 195 |
+
},
|
| 196 |
+
"151667": {
|
| 197 |
+
"content": "<think>",
|
| 198 |
+
"lstrip": false,
|
| 199 |
+
"normalized": false,
|
| 200 |
+
"rstrip": false,
|
| 201 |
+
"single_word": false,
|
| 202 |
+
"special": false
|
| 203 |
+
},
|
| 204 |
+
"151668": {
|
| 205 |
+
"content": "</think>",
|
| 206 |
+
"lstrip": false,
|
| 207 |
+
"normalized": false,
|
| 208 |
+
"rstrip": false,
|
| 209 |
+
"single_word": false,
|
| 210 |
+
"special": false
|
| 211 |
+
}
|
| 212 |
+
},
|
| 213 |
+
"additional_special_tokens": [
|
| 214 |
+
"<|im_start|>",
|
| 215 |
+
"<|im_end|>",
|
| 216 |
+
"<|object_ref_start|>",
|
| 217 |
+
"<|object_ref_end|>",
|
| 218 |
+
"<|box_start|>",
|
| 219 |
+
"<|box_end|>",
|
| 220 |
+
"<|quad_start|>",
|
| 221 |
+
"<|quad_end|>",
|
| 222 |
+
"<|vision_start|>",
|
| 223 |
+
"<|vision_end|>",
|
| 224 |
+
"<|vision_pad|>",
|
| 225 |
+
"<|image_pad|>",
|
| 226 |
+
"<|video_pad|>"
|
| 227 |
+
],
|
| 228 |
+
"bos_token": null,
|
| 229 |
+
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}",
|
| 230 |
+
"clean_up_tokenization_spaces": false,
|
| 231 |
+
"eos_token": "<|im_end|>",
|
| 232 |
+
"errors": "replace",
|
| 233 |
+
"model_max_length": 1010000,
|
| 234 |
+
"pad_token": "<|endoftext|>",
|
| 235 |
+
"split_special_tokens": false,
|
| 236 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 237 |
+
"unk_token": null,
|
| 238 |
+
"add_bos_token": false
|
| 239 |
+
}
|
models/Qwen3-4B-Instruct-2507/vocab.json
ADDED
|
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|
models/Qwen3-Embedding-0.6B/.gitattributes
ADDED
|
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*.7z filter=lfs diff=lfs merge=lfs -text
|
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*.arrow filter=lfs diff=lfs merge=lfs -text
|
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+
*.bin filter=lfs diff=lfs merge=lfs -text
|
| 4 |
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*.bz2 filter=lfs diff=lfs merge=lfs -text
|
| 5 |
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*.ckpt filter=lfs diff=lfs merge=lfs -text
|
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|
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*.gz filter=lfs diff=lfs merge=lfs -text
|
| 8 |
+
*.h5 filter=lfs diff=lfs merge=lfs -text
|
| 9 |
+
*.joblib filter=lfs diff=lfs merge=lfs -text
|
| 10 |
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
| 11 |
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*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
| 12 |
+
*.model filter=lfs diff=lfs merge=lfs -text
|
| 13 |
+
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
| 14 |
+
*.npy filter=lfs diff=lfs merge=lfs -text
|
| 15 |
+
*.npz filter=lfs diff=lfs merge=lfs -text
|
| 16 |
+
*.onnx filter=lfs diff=lfs merge=lfs -text
|
| 17 |
+
*.ot filter=lfs diff=lfs merge=lfs -text
|
| 18 |
+
*.parquet filter=lfs diff=lfs merge=lfs -text
|
| 19 |
+
*.pb filter=lfs diff=lfs merge=lfs -text
|
| 20 |
+
*.pickle filter=lfs diff=lfs merge=lfs -text
|
| 21 |
+
*.pkl filter=lfs diff=lfs merge=lfs -text
|
| 22 |
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*.pt filter=lfs diff=lfs merge=lfs -text
|
| 23 |
+
*.pth filter=lfs diff=lfs merge=lfs -text
|
| 24 |
+
*.rar filter=lfs diff=lfs merge=lfs -text
|
| 25 |
+
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
| 26 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
| 27 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
| 28 |
+
*.tar filter=lfs diff=lfs merge=lfs -text
|
| 29 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
| 30 |
+
*.tgz filter=lfs diff=lfs merge=lfs -text
|
| 31 |
+
*.wasm filter=lfs diff=lfs merge=lfs -text
|
| 32 |
+
*.xz filter=lfs diff=lfs merge=lfs -text
|
| 33 |
+
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
+
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 36 |
+
tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
models/Qwen3-Embedding-0.6B/1_Pooling/config.json
ADDED
|
@@ -0,0 +1,10 @@
|
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|
| 1 |
+
{
|
| 2 |
+
"word_embedding_dimension": 1024,
|
| 3 |
+
"pooling_mode_cls_token": false,
|
| 4 |
+
"pooling_mode_mean_tokens": false,
|
| 5 |
+
"pooling_mode_max_tokens": false,
|
| 6 |
+
"pooling_mode_mean_sqrt_len_tokens": false,
|
| 7 |
+
"pooling_mode_weightedmean_tokens": false,
|
| 8 |
+
"pooling_mode_lasttoken": true,
|
| 9 |
+
"include_prompt": true
|
| 10 |
+
}
|
models/Qwen3-Embedding-0.6B/README.md
ADDED
|
@@ -0,0 +1,292 @@
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|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
base_model:
|
| 4 |
+
- Qwen/Qwen3-0.6B-Base
|
| 5 |
+
tags:
|
| 6 |
+
- transformers
|
| 7 |
+
- sentence-transformers
|
| 8 |
+
- sentence-similarity
|
| 9 |
+
- feature-extraction
|
| 10 |
+
- text-embeddings-inference
|
| 11 |
+
---
|
| 12 |
+
# Qwen3-Embedding-0.6B
|
| 13 |
+
|
| 14 |
+
<p align="center">
|
| 15 |
+
<img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/logo_qwen3.png" width="400"/>
|
| 16 |
+
<p>
|
| 17 |
+
|
| 18 |
+
## Highlights
|
| 19 |
+
|
| 20 |
+
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.
|
| 21 |
+
|
| 22 |
+
**Exceptional Versatility**: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks **No.1** in the MTEB multilingual leaderboard (as of June 5, 2025, score **70.58**), while the reranking model excels in various text retrieval scenarios.
|
| 23 |
+
|
| 24 |
+
**Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios.
|
| 25 |
+
|
| 26 |
+
**Multilingual Capability**: The Qwen3 Embedding series offer support for over 100 languages, thanks to the multilingual capabilites of Qwen3 models. This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities.
|
| 27 |
+
|
| 28 |
+
## Model Overview
|
| 29 |
+
|
| 30 |
+
**Qwen3-Embedding-0.6B** has the following features:
|
| 31 |
+
|
| 32 |
+
- Model Type: Text Embedding
|
| 33 |
+
- Supported Languages: 100+ Languages
|
| 34 |
+
- Number of Parameters: 0.6B
|
| 35 |
+
- Context Length: 32k
|
| 36 |
+
- Embedding Dimension: Up to 1024, supports user-defined output dimensions ranging from 32 to 1024
|
| 37 |
+
|
| 38 |
+
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3-embedding/), [GitHub](https://github.com/QwenLM/Qwen3-Embedding).
|
| 39 |
+
|
| 40 |
+
## Qwen3 Embedding Series Model list
|
| 41 |
+
|
| 42 |
+
| Model Type | Models | Size | Layers | Sequence Length | Embedding Dimension | MRL Support | Instruction Aware |
|
| 43 |
+
|------------------|----------------------|------|--------|-----------------|---------------------|-------------|----------------|
|
| 44 |
+
| Text Embedding | [Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) | 0.6B | 28 | 32K | 1024 | Yes | Yes |
|
| 45 |
+
| Text Embedding | [Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B) | 4B | 36 | 32K | 2560 | Yes | Yes |
|
| 46 |
+
| Text Embedding | [Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B) | 8B | 36 | 32K | 4096 | Yes | Yes |
|
| 47 |
+
| Text Reranking | [Qwen3-Reranker-0.6B](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) | 0.6B | 28 | 32K | - | - | Yes |
|
| 48 |
+
| Text Reranking | [Qwen3-Reranker-4B](https://huggingface.co/Qwen/Qwen3-Reranker-4B) | 4B | 36 | 32K | - | - | Yes |
|
| 49 |
+
| Text Reranking | [Qwen3-Reranker-8B](https://huggingface.co/Qwen/Qwen3-Reranker-8B) | 8B | 36 | 32K | - | - | Yes |
|
| 50 |
+
|
| 51 |
+
> **Note**:
|
| 52 |
+
> - `MRL Support` indicates whether the embedding model supports custom dimensions for the final embedding.
|
| 53 |
+
> - `Instruction Aware` notes whether the embedding or reranking model supports customizing the input instruction according to different tasks.
|
| 54 |
+
> - Our evaluation indicates that, for most downstream tasks, using instructions (instruct) typically yields an improvement of 1% to 5% compared to not using them. Therefore, we recommend that developers create tailored instructions specific to their tasks and scenarios. In multilingual contexts, we also advise users to write their instructions in English, as most instructions utilized during the model training process were originally written in English.
|
| 55 |
+
|
| 56 |
+
## Usage
|
| 57 |
+
|
| 58 |
+
With Transformers versions earlier than 4.51.0, you may encounter the following error:
|
| 59 |
+
```
|
| 60 |
+
KeyError: 'qwen3'
|
| 61 |
+
```
|
| 62 |
+
|
| 63 |
+
### Sentence Transformers Usage
|
| 64 |
+
|
| 65 |
+
```python
|
| 66 |
+
# Requires transformers>=4.51.0
|
| 67 |
+
# Requires sentence-transformers>=2.7.0
|
| 68 |
+
|
| 69 |
+
from sentence_transformers import SentenceTransformer
|
| 70 |
+
|
| 71 |
+
# Load the model
|
| 72 |
+
model = SentenceTransformer("Qwen/Qwen3-Embedding-0.6B")
|
| 73 |
+
|
| 74 |
+
# We recommend enabling flash_attention_2 for better acceleration and memory saving,
|
| 75 |
+
# together with setting `padding_side` to "left":
|
| 76 |
+
# model = SentenceTransformer(
|
| 77 |
+
# "Qwen/Qwen3-Embedding-0.6B",
|
| 78 |
+
# model_kwargs={"attn_implementation": "flash_attention_2", "device_map": "auto"},
|
| 79 |
+
# tokenizer_kwargs={"padding_side": "left"},
|
| 80 |
+
# )
|
| 81 |
+
|
| 82 |
+
# The queries and documents to embed
|
| 83 |
+
queries = [
|
| 84 |
+
"What is the capital of China?",
|
| 85 |
+
"Explain gravity",
|
| 86 |
+
]
|
| 87 |
+
documents = [
|
| 88 |
+
"The capital of China is Beijing.",
|
| 89 |
+
"Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun.",
|
| 90 |
+
]
|
| 91 |
+
|
| 92 |
+
# Encode the queries and documents. Note that queries benefit from using a prompt
|
| 93 |
+
# Here we use the prompt called "query" stored under `model.prompts`, but you can
|
| 94 |
+
# also pass your own prompt via the `prompt` argument
|
| 95 |
+
query_embeddings = model.encode(queries, prompt_name="query")
|
| 96 |
+
document_embeddings = model.encode(documents)
|
| 97 |
+
|
| 98 |
+
# Compute the (cosine) similarity between the query and document embeddings
|
| 99 |
+
similarity = model.similarity(query_embeddings, document_embeddings)
|
| 100 |
+
print(similarity)
|
| 101 |
+
# tensor([[0.7646, 0.1414],
|
| 102 |
+
# [0.1355, 0.6000]])
|
| 103 |
+
```
|
| 104 |
+
|
| 105 |
+
### Transformers Usage
|
| 106 |
+
|
| 107 |
+
```python
|
| 108 |
+
# Requires transformers>=4.51.0
|
| 109 |
+
|
| 110 |
+
import torch
|
| 111 |
+
import torch.nn.functional as F
|
| 112 |
+
|
| 113 |
+
from torch import Tensor
|
| 114 |
+
from transformers import AutoTokenizer, AutoModel
|
| 115 |
+
|
| 116 |
+
|
| 117 |
+
def last_token_pool(last_hidden_states: Tensor,
|
| 118 |
+
attention_mask: Tensor) -> Tensor:
|
| 119 |
+
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
|
| 120 |
+
if left_padding:
|
| 121 |
+
return last_hidden_states[:, -1]
|
| 122 |
+
else:
|
| 123 |
+
sequence_lengths = attention_mask.sum(dim=1) - 1
|
| 124 |
+
batch_size = last_hidden_states.shape[0]
|
| 125 |
+
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
|
| 126 |
+
|
| 127 |
+
|
| 128 |
+
def get_detailed_instruct(task_description: str, query: str) -> str:
|
| 129 |
+
return f'Instruct: {task_description}\nQuery:{query}'
|
| 130 |
+
|
| 131 |
+
# Each query must come with a one-sentence instruction that describes the task
|
| 132 |
+
task = 'Given a web search query, retrieve relevant passages that answer the query'
|
| 133 |
+
|
| 134 |
+
queries = [
|
| 135 |
+
get_detailed_instruct(task, 'What is the capital of China?'),
|
| 136 |
+
get_detailed_instruct(task, 'Explain gravity')
|
| 137 |
+
]
|
| 138 |
+
# No need to add instruction for retrieval documents
|
| 139 |
+
documents = [
|
| 140 |
+
"The capital of China is Beijing.",
|
| 141 |
+
"Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
|
| 142 |
+
]
|
| 143 |
+
input_texts = queries + documents
|
| 144 |
+
|
| 145 |
+
tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen3-Embedding-0.6B', padding_side='left')
|
| 146 |
+
model = AutoModel.from_pretrained('Qwen/Qwen3-Embedding-0.6B')
|
| 147 |
+
|
| 148 |
+
# We recommend enabling flash_attention_2 for better acceleration and memory saving.
|
| 149 |
+
# model = AutoModel.from_pretrained('Qwen/Qwen3-Embedding-0.6B', attn_implementation="flash_attention_2", torch_dtype=torch.float16).cuda()
|
| 150 |
+
|
| 151 |
+
max_length = 8192
|
| 152 |
+
|
| 153 |
+
# Tokenize the input texts
|
| 154 |
+
batch_dict = tokenizer(
|
| 155 |
+
input_texts,
|
| 156 |
+
padding=True,
|
| 157 |
+
truncation=True,
|
| 158 |
+
max_length=max_length,
|
| 159 |
+
return_tensors="pt",
|
| 160 |
+
)
|
| 161 |
+
batch_dict.to(model.device)
|
| 162 |
+
outputs = model(**batch_dict)
|
| 163 |
+
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
|
| 164 |
+
|
| 165 |
+
# normalize embeddings
|
| 166 |
+
embeddings = F.normalize(embeddings, p=2, dim=1)
|
| 167 |
+
scores = (embeddings[:2] @ embeddings[2:].T)
|
| 168 |
+
print(scores.tolist())
|
| 169 |
+
# [[0.7645568251609802, 0.14142508804798126], [0.13549736142158508, 0.5999549627304077]]
|
| 170 |
+
```
|
| 171 |
+
|
| 172 |
+
### vLLM Usage
|
| 173 |
+
|
| 174 |
+
```python
|
| 175 |
+
# Requires vllm>=0.8.5
|
| 176 |
+
import torch
|
| 177 |
+
import vllm
|
| 178 |
+
from vllm import LLM
|
| 179 |
+
|
| 180 |
+
def get_detailed_instruct(task_description: str, query: str) -> str:
|
| 181 |
+
return f'Instruct: {task_description}\nQuery:{query}'
|
| 182 |
+
|
| 183 |
+
# Each query must come with a one-sentence instruction that describes the task
|
| 184 |
+
task = 'Given a web search query, retrieve relevant passages that answer the query'
|
| 185 |
+
|
| 186 |
+
queries = [
|
| 187 |
+
get_detailed_instruct(task, 'What is the capital of China?'),
|
| 188 |
+
get_detailed_instruct(task, 'Explain gravity')
|
| 189 |
+
]
|
| 190 |
+
# No need to add instruction for retrieval documents
|
| 191 |
+
documents = [
|
| 192 |
+
"The capital of China is Beijing.",
|
| 193 |
+
"Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
|
| 194 |
+
]
|
| 195 |
+
input_texts = queries + documents
|
| 196 |
+
|
| 197 |
+
model = LLM(model="Qwen/Qwen3-Embedding-0.6B", task="embed")
|
| 198 |
+
|
| 199 |
+
outputs = model.embed(input_texts)
|
| 200 |
+
embeddings = torch.tensor([o.outputs.embedding for o in outputs])
|
| 201 |
+
scores = (embeddings[:2] @ embeddings[2:].T)
|
| 202 |
+
print(scores.tolist())
|
| 203 |
+
# [[0.7620252966880798, 0.14078938961029053], [0.1358368694782257, 0.6013815999031067]]
|
| 204 |
+
```
|
| 205 |
+
|
| 206 |
+
📌 **Tip**: We recommend that developers customize the `instruct` according to their specific scenarios, tasks, and languages. Our tests have shown that in most retrieval scenarios, not using an `instruct` on the query side can lead to a drop in retrieval performance by approximately 1% to 5%.
|
| 207 |
+
|
| 208 |
+
### Text Embeddings Inference (TEI) Usage
|
| 209 |
+
|
| 210 |
+
You can either run / deploy TEI on NVIDIA GPUs as:
|
| 211 |
+
|
| 212 |
+
```bash
|
| 213 |
+
docker run --gpus all -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.7.2 --model-id Qwen/Qwen3-Embedding-0.6B --dtype float16
|
| 214 |
+
```
|
| 215 |
+
|
| 216 |
+
Or on CPU devices as:
|
| 217 |
+
|
| 218 |
+
```bash
|
| 219 |
+
docker run -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.7.2 --model-id Qwen/Qwen3-Embedding-0.6B
|
| 220 |
+
```
|
| 221 |
+
|
| 222 |
+
And then, generate the embeddings sending a HTTP POST request as:
|
| 223 |
+
|
| 224 |
+
```bash
|
| 225 |
+
curl http://localhost:8080/embed \
|
| 226 |
+
-X POST \
|
| 227 |
+
-d '{"inputs": ["Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: What is the capital of China?", "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: Explain gravity"]}' \
|
| 228 |
+
-H "Content-Type: application/json"
|
| 229 |
+
```
|
| 230 |
+
|
| 231 |
+
## Evaluation
|
| 232 |
+
|
| 233 |
+
### MTEB (Multilingual)
|
| 234 |
+
|
| 235 |
+
| Model | Size | Mean (Task) | Mean (Type) | Bitxt Mining | Class. | Clust. | Inst. Retri. | Multi. Class. | Pair. Class. | Rerank | Retri. | STS |
|
| 236 |
+
|----------------------------------|:-------:|:-------------:|:-------------:|:--------------:|:--------:|:--------:|:--------------:|:---------------:|:--------------:|:--------:|:--------:|:------:|
|
| 237 |
+
| NV-Embed-v2 | 7B | 56.29 | 49.58 | 57.84 | 57.29 | 40.80 | 1.04 | 18.63 | 78.94 | 63.82 | 56.72 | 71.10|
|
| 238 |
+
| GritLM-7B | 7B | 60.92 | 53.74 | 70.53 | 61.83 | 49.75 | 3.45 | 22.77 | 79.94 | 63.78 | 58.31 | 73.33|
|
| 239 |
+
| BGE-M3 | 0.6B | 59.56 | 52.18 | 79.11 | 60.35 | 40.88 | -3.11 | 20.1 | 80.76 | 62.79 | 54.60 | 74.12|
|
| 240 |
+
| multilingual-e5-large-instruct | 0.6B | 63.22 | 55.08 | 80.13 | 64.94 | 50.75 | -0.40 | 22.91 | 80.86 | 62.61 | 57.12 | 76.81|
|
| 241 |
+
| gte-Qwen2-1.5B-instruct | 1.5B | 59.45 | 52.69 | 62.51 | 58.32 | 52.05 | 0.74 | 24.02 | 81.58 | 62.58 | 60.78 | 71.61|
|
| 242 |
+
| gte-Qwen2-7b-Instruct | 7B | 62.51 | 55.93 | 73.92 | 61.55 | 52.77 | 4.94 | 25.48 | 85.13 | 65.55 | 60.08 | 73.98|
|
| 243 |
+
| text-embedding-3-large | - | 58.93 | 51.41 | 62.17 | 60.27 | 46.89 | -2.68 | 22.03 | 79.17 | 63.89 | 59.27 | 71.68|
|
| 244 |
+
| Cohere-embed-multilingual-v3.0 | - | 61.12 | 53.23 | 70.50 | 62.95 | 46.89 | -1.89 | 22.74 | 79.88 | 64.07 | 59.16 | 74.80|
|
| 245 |
+
| Gemini Embedding | - | 68.37 | 59.59 | 79.28 | 71.82 | 54.59 | 5.18 | **29.16** | 83.63 | 65.58 | 67.71 | 79.40|
|
| 246 |
+
| **Qwen3-Embedding-0.6B** | 0.6B | 64.33 | 56.00 | 72.22 | 66.83 | 52.33 | 5.09 | 24.59 | 80.83 | 61.41 | 64.64 | 76.17|
|
| 247 |
+
| **Qwen3-Embedding-4B** | 4B | 69.45 | 60.86 | 79.36 | 72.33 | 57.15 | **11.56** | 26.77 | 85.05 | 65.08 | 69.60 | 80.86|
|
| 248 |
+
| **Qwen3-Embedding-8B** | 8B | **70.58** | **61.69** | **80.89** | **74.00** | **57.65** | 10.06 | 28.66 | **86.40** | **65.63** | **70.88** | **81.08** |
|
| 249 |
+
|
| 250 |
+
> **Note**: For compared models, the scores are retrieved from MTEB online [leaderboard](https://huggingface.co/spaces/mteb/leaderboard) on May 24th, 2025.
|
| 251 |
+
|
| 252 |
+
### MTEB (Eng v2)
|
| 253 |
+
|
| 254 |
+
| MTEB English / Models | Param. | Mean(Task) | Mean(Type) | Class. | Clust. | Pair Class. | Rerank. | Retri. | STS | Summ. |
|
| 255 |
+
|--------------------------------|:--------:|:------------:|:------------:|:--------:|:--------:|:-------------:|:---------:|:--------:|:-------:|:-------:|
|
| 256 |
+
| multilingual-e5-large-instruct | 0.6B | 65.53 | 61.21 | 75.54 | 49.89 | 86.24 | 48.74 | 53.47 | 84.72 | 29.89 |
|
| 257 |
+
| NV-Embed-v2 | 7.8B | 69.81 | 65.00 | 87.19 | 47.66 | 88.69 | 49.61 | 62.84 | 83.82 | 35.21 |
|
| 258 |
+
| GritLM-7B | 7.2B | 67.07 | 63.22 | 81.25 | 50.82 | 87.29 | 49.59 | 54.95 | 83.03 | 35.65 |
|
| 259 |
+
| gte-Qwen2-1.5B-instruct | 1.5B | 67.20 | 63.26 | 85.84 | 53.54 | 87.52 | 49.25 | 50.25 | 82.51 | 33.94 |
|
| 260 |
+
| stella_en_1.5B_v5 | 1.5B | 69.43 | 65.32 | 89.38 | 57.06 | 88.02 | 50.19 | 52.42 | 83.27 | 36.91 |
|
| 261 |
+
| gte-Qwen2-7B-instruct | 7.6B | 70.72 | 65.77 | 88.52 | 58.97 | 85.9 | 50.47 | 58.09 | 82.69 | 35.74 |
|
| 262 |
+
| gemini-embedding-exp-03-07 | - | 73.3 | 67.67 | 90.05 | 59.39 | 87.7 | 48.59 | 64.35 | 85.29 | 38.28 |
|
| 263 |
+
| **Qwen3-Embedding-0.6B** | 0.6B | 70.70 | 64.88 | 85.76 | 54.05 | 84.37 | 48.18 | 61.83 | 86.57 | 33.43 |
|
| 264 |
+
| **Qwen3-Embedding-4B** | 4B | 74.60 | 68.10 | 89.84 | 57.51 | 87.01 | 50.76 | 68.46 | 88.72 | 34.39 |
|
| 265 |
+
| **Qwen3-Embedding-8B** | 8B | 75.22 | 68.71 | 90.43 | 58.57 | 87.52 | 51.56 | 69.44 | 88.58 | 34.83 |
|
| 266 |
+
|
| 267 |
+
### C-MTEB (MTEB Chinese)
|
| 268 |
+
|
| 269 |
+
| C-MTEB | Param. | Mean(Task) | Mean(Type) | Class. | Clust. | Pair Class. | Rerank. | Retr. | STS |
|
| 270 |
+
|------------------|--------|------------|------------|--------|--------|-------------|---------|-------|-------|
|
| 271 |
+
| multilingual-e5-large-instruct | 0.6B | 58.08 | 58.24 | 69.80 | 48.23 | 64.52 | 57.45 | 63.65 | 45.81 |
|
| 272 |
+
| bge-multilingual-gemma2 | 9B | 67.64 | 75.31 | 59.30 | 86.67 | 68.28 | 73.73 | 55.19 | - |
|
| 273 |
+
| gte-Qwen2-1.5B-instruct | 1.5B | 67.12 | 67.79 | 72.53 | 54.61 | 79.5 | 68.21 | 71.86 | 60.05 |
|
| 274 |
+
| gte-Qwen2-7B-instruct | 7.6B | 71.62 | 72.19 | 75.77 | 66.06 | 81.16 | 69.24 | 75.70 | 65.20 |
|
| 275 |
+
| ritrieve_zh_v1 | 0.3B | 72.71 | 73.85 | 76.88 | 66.5 | 85.98 | 72.86 | 76.97 | 63.92 |
|
| 276 |
+
| **Qwen3-Embedding-0.6B** | 0.6B | 66.33 | 67.45 | 71.40 | 68.74 | 76.42 | 62.58 | 71.03 | 54.52 |
|
| 277 |
+
| **Qwen3-Embedding-4B** | 4B | 72.27 | 73.51 | 75.46 | 77.89 | 83.34 | 66.05 | 77.03 | 61.26 |
|
| 278 |
+
| **Qwen3-Embedding-8B** | 8B | 73.84 | 75.00 | 76.97 | 80.08 | 84.23 | 66.99 | 78.21 | 63.53 |
|
| 279 |
+
|
| 280 |
+
|
| 281 |
+
## Citation
|
| 282 |
+
|
| 283 |
+
If you find our work helpful, feel free to give us a cite.
|
| 284 |
+
|
| 285 |
+
```
|
| 286 |
+
@article{qwen3embedding,
|
| 287 |
+
title={Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models},
|
| 288 |
+
author={Zhang, Yanzhao and Li, Mingxin and Long, Dingkun and Zhang, Xin and Lin, Huan and Yang, Baosong and Xie, Pengjun and Yang, An and Liu, Dayiheng and Lin, Junyang and Huang, Fei and Zhou, Jingren},
|
| 289 |
+
journal={arXiv preprint arXiv:2506.05176},
|
| 290 |
+
year={2025}
|
| 291 |
+
}
|
| 292 |
+
```
|
models/Qwen3-Embedding-0.6B/config.json
ADDED
|
@@ -0,0 +1,30 @@
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|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"Qwen3ForCausalLM"
|
| 4 |
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],
|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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|
| 29 |
+
"vocab_size": 151669
|
| 30 |
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}
|
models/Qwen3-Embedding-0.6B/config_sentence_transformers.json
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
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|
| 1 |
+
{
|
| 2 |
+
"prompts": {
|
| 3 |
+
"query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:",
|
| 4 |
+
"document": ""
|
| 5 |
+
},
|
| 6 |
+
"default_prompt_name": null,
|
| 7 |
+
"similarity_fn_name": "cosine"
|
| 8 |
+
}
|
models/Qwen3-Embedding-0.6B/generation_config.json
ADDED
|
@@ -0,0 +1,6 @@
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|
| 1 |
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{
|
| 2 |
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"bos_token_id": 151643,
|
| 3 |
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"eos_token_id": 151643,
|
| 4 |
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"max_new_tokens": 2048,
|
| 5 |
+
"transformers_version": "4.51.3"
|
| 6 |
+
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|
models/Qwen3-Embedding-0.6B/merges.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
models/Qwen3-Embedding-0.6B/model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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| 2 |
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| 3 |
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size 1191586416
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models/Qwen3-Embedding-0.6B/modules.json
ADDED
|
@@ -0,0 +1,20 @@
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| 1 |
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[
|
| 2 |
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{
|
| 3 |
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"idx": 0,
|
| 4 |
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"name": "0",
|
| 5 |
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|
| 6 |
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"type": "sentence_transformers.models.Transformer"
|
| 7 |
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|
| 8 |
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{
|
| 9 |
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"idx": 1,
|
| 10 |
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"name": "1",
|
| 11 |
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"path": "1_Pooling",
|
| 12 |
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"type": "sentence_transformers.models.Pooling"
|
| 13 |
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},
|
| 14 |
+
{
|
| 15 |
+
"idx": 2,
|
| 16 |
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"name": "2",
|
| 17 |
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"path": "2_Normalize",
|
| 18 |
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"type": "sentence_transformers.models.Normalize"
|
| 19 |
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}
|
| 20 |
+
]
|
models/Qwen3-Embedding-0.6B/tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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| 3 |
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size 11423705
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models/Qwen3-Embedding-0.6B/tokenizer_config.json
ADDED
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@@ -0,0 +1,240 @@
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|
| 1 |
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{
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| 28 |
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"lstrip": false,
|
| 200 |
+
"normalized": false,
|
| 201 |
+
"rstrip": false,
|
| 202 |
+
"single_word": false,
|
| 203 |
+
"special": false
|
| 204 |
+
},
|
| 205 |
+
"151668": {
|
| 206 |
+
"content": "</think>",
|
| 207 |
+
"lstrip": false,
|
| 208 |
+
"normalized": false,
|
| 209 |
+
"rstrip": false,
|
| 210 |
+
"single_word": false,
|
| 211 |
+
"special": false
|
| 212 |
+
}
|
| 213 |
+
},
|
| 214 |
+
"additional_special_tokens": [
|
| 215 |
+
"<|im_start|>",
|
| 216 |
+
"<|im_end|>",
|
| 217 |
+
"<|object_ref_start|>",
|
| 218 |
+
"<|object_ref_end|>",
|
| 219 |
+
"<|box_start|>",
|
| 220 |
+
"<|box_end|>",
|
| 221 |
+
"<|quad_start|>",
|
| 222 |
+
"<|quad_end|>",
|
| 223 |
+
"<|vision_start|>",
|
| 224 |
+
"<|vision_end|>",
|
| 225 |
+
"<|vision_pad|>",
|
| 226 |
+
"<|image_pad|>",
|
| 227 |
+
"<|video_pad|>"
|
| 228 |
+
],
|
| 229 |
+
"bos_token": null,
|
| 230 |
+
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
|
| 231 |
+
"clean_up_tokenization_spaces": false,
|
| 232 |
+
"eos_token": "<|im_end|>",
|
| 233 |
+
"errors": "replace",
|
| 234 |
+
"extra_special_tokens": {},
|
| 235 |
+
"model_max_length": 131072,
|
| 236 |
+
"pad_token": "<|endoftext|>",
|
| 237 |
+
"split_special_tokens": false,
|
| 238 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 239 |
+
"unk_token": null
|
| 240 |
+
}
|
models/Qwen3-Embedding-0.6B/vocab.json
ADDED
|
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|
|